Fostering student wellbeing in the postsecondary teaching and learning environment
Bibliographic record
Abstract
The mental health and wellbeing of postsecondary students can affect motivation and academic success; however, research that examines how the academic learning environment contributes to students’ wellbeing is limited. The current research used mixed methods to explore students’ perceptions of the intersection between their learning environment and mental health and wellbeing. In Phase 1, 247 students indicated how often they experienced various supportive instructional practices. In Phase 2, in-depth interviews (n = 13) explored possible improvements in teaching and learning environments to benefit wellbeing. We developed seven key factors that contributed to a sense of wellbeing: (1) effective promotion of resources, (2) instructor care, (3) course and assessment design that considers workload, (4) flexibility in policy and practice, (5) reducing stigma, (6) peer support, and (7) recognising mental health as a shared responsibility in the university community. Our findings have implications for how instructors and institutions can foster student wellbeing in learning environments through course design, instructional strategies, and cultivating awareness and openness around campus mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".